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· via Google Cloud Customer Stories· Customer story

Augment Code boosts developer productivity and code troubleshooting with AI on Vertex AI

Augment · Tech & Comms · United States

Headline result

The source does not publish a single comparable figure. Reported results are listed below.

Summary

Augment, a software development tools company, aimed to accelerate code understanding and troubleshooting in complex codebases.

They integrated Anthropic's Claude 3.5 Sonnet model with Google Cloud's Vertex AI to provide natural language code chat and coding assistance.

This integration enabled rapid switching to stable production models and secured hosting within Google Cloud.

Results included 20-30% productivity gains, reduction of onboarding time from weeks to days, and improved developer workflow speed.

The problem

  1. 01Accelerate code understanding and troubleshooting in growing, complex codebases with multiple independent contributors.
  2. 02Reduce lengthy developer onboarding times to speed productivity.
  3. 03Provide secure and scalable AI-assisted coding tools for enterprise customers.

What they built

  1. 01Integrated Anthropic Claude 3.5 Sonnet model directly with Google Cloud Vertex AI to enable natural language chat for code understanding and assistance.
  2. 02Switched production to Claude 3.5 Sonnet on Vertex AI rapidly within a weekend due to easy implementation.
  3. 03Developed secure cloud hosting model to protect enterprise intellectual property.
  4. 04Built personalized code indexing system on Google Cloud for high-speed, low-latency AI inference with GPU optimization.

Reported results

  1. 01Achieved 20-30% productivity improvements for developers using AI-assisted troubleshooting.
  2. 02Reduced developer onboarding time from weeks to 1-2 days, accelerating time to contribution.
  3. 03Maintained high security standards by running AI models entirely within Google Cloud.
  4. 04Enabled faster resolution of code issues, allowing developers to focus more on higher-value engineering tasks.
  • Improved developer troubleshooting productivity
  • Reduced developer onboarding time
  • Kept AI models within Google Cloud
  • Enabled natural language code assistance

Source

Google Cloud Customer Stories

Customer story

Read the original(opens in a new tab)

How this deployment compares

Computed from the library · data 10 Nov 2026

This is the only Augment deployment in the library. It is one of 30 code assistant deployments, 10 of them in Tech & Comms.

Augment in the library

The only Augment deployment in the library

Same pattern, whole library

One of 30 code assistant deployments

  • Tech & Comms accounts for 10 of them (33%), more than any other industry.
  • Only 12 of the 30 put a number on the result (40%).
Provider mix
  • AWS53%
  • Microsoft30%
  • GCP17%
Most-used technology
  • Amazon Q Developer 8
  • Amazon Bedrock AgentCore 5
  • Amazon Bedrock 4
  • Amazon EKS 3
  • Vertex AI 3
  • AWS Lambda 3

Outlined: used in this record.

Shares describe this library, not the whole market.

Code assistant analysis

Results against peers

What peers report

  • Time & speed42 figures, Tech & Comms · middle half 33%–75%60%median reduction
  • Customer experience50 figures, all industries · middle half 20%–90%28%median increase
  • Risk, reliability & safety13 figures, all industries · middle half 60%–90%70%median reduction

Peer figures are as reported by their sources; percentage claims only.

How we checked this record

Evidence tier: supported

A credible source with a named customer, but not the organization's own account.

Named organization
Augment
Who published the source
The organization itself
Deployment status
The source does not say
Quantified result
The source reports at least one figure
Summary checked against source
5 / 5 agreement

Last re-checked 13 Jun 2026.

This record is evidence for

Insight · industryTech & Comms dossierOne of 402 Tech & Comms deployments. See which patterns dominate there and what they report.Insight · patternCode assistant30 deployments share this pattern. See where it is used and how it is built.Trend · momentumBuilding pattern8 new deployments in the last six months, against 8 in the six before.

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